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المؤلفYuan, Xin
المؤلفYao, Haipeng
المؤلفWang, Jingjing
المؤلفMai, Tianle
المؤلفGuizani, Mohsen
تاريخ الإتاحة2022-10-30T19:00:58Z
تاريخ النشر2021-09-01
اسم المنشورIEEE Transactions on Cognitive Communications and Networking
المعرّفhttp://dx.doi.org/10.1109/TCCN.2021.3065463
الاقتباسYuan, X., Yao, H., Wang, J., Mai, T., & Guizani, M. (2021). Artificial intelligence empowered QoS-oriented network association for next-generation mobile networks. IEEE Transactions on Cognitive Communications and Networking, 7(3), 856-870.‏
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85102677076&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/35604
الملخصThe increasing complexity and dynamics of 5G mobile networks have brought revolutionary changes in its modeling and control, where efficient routing and resource allocation strategies become beneficial. Software-Defined Network (SDN) makes it possible to achieve the automatic management of network resources. Relying on the powerful decision-making capability of SDNs, network association can be flexibly implemented for adapting to the dynamic of the real-time network status. In this paper, we first construct a jitter graph-based network model as well as a Poisson process-based traffic model in the context of 5G mobile networks. Second, we solve the problem of QoS routing with resource allocation based on queueing theory using a low computational complexity greedy algorithm, which takes finding a feasible path set as the main task and resource allocation as the auxiliary task. Finally, we design a QoS-oriented adaptive routing scheme based on Deep Reinforcement Learning (DRL) SPACE, which is a DRL architecture with parameterized action space, in order to find an optimal path from the source to the destination. To validate the feasibility of the greedy QoS routing strategy with resource allocation, we make a numerical packet-level simulation to model a M/M/C/N queuing system. Moreover, extensive simulation results demonstrate that our proposed routing strategy is able to improve the traffic's QoS metrics, such as the packet loss ratio and queueing delay.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوع5G
deep reinforcement learning
QoS routing
resource allocation
software-defined network
العنوانArtificial Intelligence Empowered QoS-Oriented Network Association for Next-Generation Mobile Networks
النوعArticle
الصفحات856-870
رقم العدد3
رقم المجلد7
dc.accessType Abstract Only


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